Spoken keyword search system using improved ASR engine and novel template-based keyword scoring

Abstract

Keyword search for spoken documents has become more and more important nowadays due to the increasing amount of spoken data. The typical system makes use of an Automatic Speech Recognition system (ASR) and information retrieval methods. While a number of studies have been done to get the optimal system performance, KeyWord Search (KWS) systems still suffer from two main drawbacks. First, the system performance depends strongly on the ASR transcripts which are inherently inexact. Due to the speech signal variabilities, ASR systems are far from being powerful. Second, KWS systems make detection decisions based on the lattice-based posterior probability which is incomparable across keywords. In addition, posterior probabilities of true detection usually fall into different ranges which decrease the spotting performance. This paper considers the problems of ASR transcriptions and keyword detection decision based on posterior probabilities. More specifically, we propose to enhance the ASR transcripts accuracy by introducing a new ASR architecture in which we integrate data augmentation and ensemble learning techniques into a single framework. In addition, we proposed a novel keyword rescoring method that provides scores from a new perspective. Precisely, inspired by template-based KWS approach, scores of similarity between the detected keywords are computed by computing the distance between the acoustic features and are used as new scores for decision. Experiments on French and English datasets show that the proposed KWS system potentially leads to more accurate keyword results than the conventional systems.